Eco-driving technology for sustainable road transport: A review
DOI: 10.1016/j.rser.2018.05.030
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Summary
This review paper addresses the critical role of eco-driving technology in achieving sustainable road transport, motivated by the need to meet global CO2 reduction targets established by the Paris Agreement. Road transport accounts for approximately 25% of global energy-related CO2 emissions, with road vehicles responsible for 75% of that share. While technological advancements in engines and vehicles offer modest efficiency gains (2–10%), eco-driving presents a low-cost, immediate intervention capable of reducing fuel consumption by up to 45%. The study aims to synthesize existing literature on the major factors influencing eco-driving, the methods used to study them, and the challenges in implementation. The authors categorize eco-driving into five primary behavioral factors: driving speed, acceleration/deceleration, idling, route choice, and vehicle accessories. Driving speed analysis reveals a U-shaped fuel consumption curve, with optimal efficiency typically occurring between 50–90 km/h depending on vehicle type. Acceleration and deceleration are identified as the most significant factors, where aggressive driving can increase fuel consumption by 15–30% on highways and up to 40% in stop-and-go traffic. Idling is highlighted as a major source of waste, with modern vehicles requiring no warm-up idling; however, driver misconceptions persist. Route choice involves trade-offs between travel time and fuel efficiency, with eco-routing algorithms showing potential savings of 2–25% by avoiding congestion and steep grades. The review also examines research methodologies, comparing laboratory tests (engine and chassis dynamometers) for their high repeatability against on-road experiments and numerical modeling, which better capture real-world variability. Key findings indicate that eco-driving training and in-vehicle feedback devices yield immediate, significant reductions in fuel consumption and CO2 emissions, though these benefits often attenuate over time due to ingrained driving habits. Acceleration/deceleration contributes the largest share of potential savings (3.5–40%), followed by route choice (2.2–25%) and driving speed (2–29%). Idling reduction offers 6–20% savings. The paper notes that current research predominantly focuses on individual vehicle performance, largely ignoring pollutant emissions other than CO2 and the systemic impacts of eco-driving at the network level. For instance, high penetration rates of eco-drivers may inadvertently increase congestion and global emissions under certain traffic conditions. The significance of this review lies in its identification of gaps in current eco-driving strategies. The authors conclude that reliance on driver behavior modification is insufficient for lasting change. Future research must focus on developing quantitative eco-driving patterns that can be integrated into vehicle hardware to ensure uniform improvements. Additionally, there is a need for more effective, long-lasting training programs and a shift in research focus toward network-level impacts and comprehensive pollutant analysis, rather than solely individual fuel savings.
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| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | OpenAlex-citations | — | — | 1 | 2026-06-17 |
| archive | success | unpaywall | — | — | 2 | 2026-06-25 |
| extract | success | pdftotext | — | — | 2 | 2026-06-26 |
| clean | success | clean | — | — | 1 | 2026-06-26 |
| chunk | success | chunk | — | — | 1 | 2026-06-26 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-26 |
| enrich | success | semantic_scholar | — | — | 5 | 2026-07-05 |
| promote | success | — | — | — | 1 | 2026-06-17 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 1 | 2026-06-25 |
| tag | success | vector_similarity | — | — | 6 | 2026-06-26 |
| verify | partial | — | — | — | 1 | 2026-06-26 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-25; verification: verified_with_issues.
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- Empirical Findings: observational prevalence
- Methodological Resource: tool software, validation psychometrics